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<p>This paper investigates the challenges of maximizing power output from photovoltaic (PV) generators under variable climatic situations, like fluctuating sunlight intensity, temperature variations, and shading. We propose a novel control strategy that merges Adaptive Particle Swarm Optimization (APSO) with Terminal Sliding Mode Control (TSMC) to accomplish efficient and robust optimal power point tracking (OPPT) under uncertainties. Extensive simulations demonstrate that the proposed APSO-TSMC controller outperforms existing algorithms, achieving a significant improvement in power harvest compared to conventional approaches.</p>

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Efficiency optimization of photovoltaic system by integrating Adaptive Particle Swarm Optimization and Terminal Sliding Mode Control

  • 期刊论文

  • 发表日期：

    2025年10月25日

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  • Houssine EL Hammedi,
  • Aymen Lachheb,
  • Jaouher Chrouta,
  • Achraf Jabeur Telmoudi,
  • Abderrahmen Zaafouri

摘要

This paper investigates the challenges of maximizing power output from photovoltaic (PV) generators under variable climatic situations, like fluctuating sunlight intensity, temperature variations, and shading. We propose a novel control strategy that merges Adaptive Particle Swarm Optimization (APSO) with Terminal Sliding Mode Control (TSMC) to accomplish efficient and robust optimal power point tracking (OPPT) under uncertainties. Extensive simulations demonstrate that the proposed APSO-TSMC controller outperforms existing algorithms, achieving a significant improvement in power harvest compared to conventional approaches.